Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
661-03-005 ISMRM Abstract

KLEAN: A Generalized Acquisition-agnostic LLR k-space Denoising Method for High-dimensional Imaging

Primary: Analysis Methods - Image Enhancement
Secondary: Acquisition & Reconstruction - Non-AI methods
661-03-005 · Noise and Artifact Mitigation in MRI · Thursday, 14 May, 1:40 PM–2:35 PM · Digital Posters Row B
Keywords: Denoising MP-PCA NORDIC Local Low-rank
Accepted
Ludwig Sichen Zhao1,2, Manuel Taso3, John Detre4,5, M. Dylan Tisdall 5
1Department of Bioengineering, University of Pennsylvania, Philadelphia, United States of America
2Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA., Philadelphia, United States of America
3Siemens Medical Solutions USA, Inc., Malvern, United States of America
4Department of Neurology, University of Pennsylvania, Philadelphia, United States of America
5Department of Radiology, University of Pennsylvania, Philadelphia, United States of America
Presenting Author: M. Dylan Tisdall

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Jones, D. K. Challenges and limitations of quantifying brain connectivity in vivo with diffusion MRI. Imaging in Medicine 2, 341–355 (2010).
2. Manzano Patron, J. P. et al. Denoising diffusion MRI: Considerations and implications for analysis. Imaging Neuroscience 2, imag–2–00060 (2024).
3. Triantafyllou, C., Polimeni, J. R. & Wald, L. L. Physiological noise and signal-to-noise ratio in fMRI with multi-channel array coils. NeuroImage 55, 597–606 (2011).
4. Raptis, C. A. et al. Building blocks for thoracic MRI: Challenges, sequences, and protocol design. Journal of Magnetic Resonance Imaging 50, 682–701 (2019).
5. Wieben, O., Francois, C. & Reeder, S. B. Cardiac MRI of ischemic heart disease at 3 T: Potential and challenges. European Journal of Radiology 65, 15–28 (2008).
6. Rajiah, P. S., François, C. J. & Leiner, T. Cardiac MRI: State of the Art. Radiology 307, e223008 (2023).
7. Wen, H., Denison, T. J., Singerman, R. W. & Balaban, R. S. The Intrinsic Signal-to-Noise Ratio in Human Cardiac Imaging at 1.5, 3, and 4 T. Journal of Magnetic Resonance 125, 65–71 (1997).
8. Veraart, J. et al. Denoising of diffusion MRI using random matrix theory. NeuroImage 142, 394–406 (2016).
9. Veraart, J., Fieremans, E. & Novikov, D. S. Diffusion MRI noise mapping using random matrix theory. Magnetic Resonance in Medicine 76, 1582–1593 (2016).
10. 10. Vizioli, L. et al. Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging. Nat Commun 12, 5181 (2021).
11. Moeller, S. et al. NOise reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing. NeuroImage 226, 117539 (2021).
12. Chang, Y. V., Vidorreta, M., Wang, Z. & Detre, J. A. 3D-accelerated, stack-of-spirals acquisitions and reconstruction of arterial spin labeling MRI. Magnetic Resonance in Medicine 78, 1405–1419 (2017).
13. Zhao, S. L. et al. Pushing the Limits of ASL Spatiotemporal Resolution using 3D Accelerated Stack-of-Spirals. in Proc. Intl. Soc. Mag. Reson. Med. vol. 34 (Hawaii, United States, 2025).

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/661-03-005